There are a considerable number of concerns when designing ML-enabled systems. Given their inherent nature, requirements for these systems must have a stronger focus on data requirements [1]}. For instance, Vogelsang [2]} based on interviews with data scientists, mentioned that requirements engineer has to identify and specify requirements regarding the quantity, quality, collection, formats and the ranges of data. This with the aim of analyzing the given dataset against requirements and business goals. Furthermore, it is well known that in ML projects, model performance measures must be specified. These measures, to some extent, depend on the quality and pre-processing of the data. However, the importance of specifying these measures in a way that customers can understand and analyze it to make decisions is often overlooked [3]}. Another concern in requirements for ML is the definition of quality properties, also known as non-functional requirements (NFRs) [4]}, [5]}. For instance, depending on the context of classification problems, the requirements engineer might have to specify ethical concerns, particularly if it is people who are classified, and define what characteristics should not be used for classification [6]}. Similarly, other quality properties such as explainability, transparency, modularity, testability, security and privacy should be clearly specified [7]}.
